Top Data & Analytics Jobs

Supports Mastercard’s ProCODE team by transforming fragmented enterprise data into operational insights and automation. The intern will develop Power BI dashboards and visualizations, work with Microsoft Fabric and data agents, use Python for analysis and automation, query large datasets with SQL, and apply DAX to translate complex data into actionable business insights. The role includes mentorship, networking, professional development, and exposure to innovative technologies.

Lead initiatives in advanced data analytics, develop frameworks, integrate AI/ML models, and foster collaboration to improve business performance and customer success.

Supports quarterly operational data collection, validation, analysis, and reporting. Investigates customer-reported data for revenue leakage, delivers performance analytics, resolves customer inquiries, provides training, documents issues, supports finance updates, and contributes to system enhancements and process improvements. The role requires strong analytical, communication, organizational, and problem-solving skills while managing tight deadlines and multiple priorities.

Leads Mastercard’s value quantification strategy, owning scalable data assets, ROI models, simulation tools, analytics, governance, and AI-enabled innovations. Partners with product, pricing, finance, sales, engineering, and customer success teams to support value-based pricing, revenue growth, customer adoption, and retention. Builds and manages a high-performing team while establishing consistent enterprise data sources, KPIs, model standards, and value storytelling capabilities.

Designs and maintains scalable ETL/ELT pipelines and batch or real-time data processing solutions using SQL, Python, and Spark. Ensures data quality, availability, and reliability through testing, monitoring, observability, and automated deployments. Supports platform upgrades, migrations, and production operations while collaborating with data and business teams. Contributes to technical reviews, documentation, architecture discussions, and engineering best practices.

Lead small data quality projects across matrixed teams, coordinate compliance communications and remediation, facilitate policy and standards education, track program KPIs, produce reports, identify risks and dependencies, and collaborate with Product, Technology, and Data Strategy teams. Support customers with data quality requirements, operational processes, issue resolution, and compliance. Use analytical and technical skills to investigate root causes, validate assumptions, and drive continuous improvement.

Design, build, and scale cloud-native data and BI solutions on Databricks and Snowflake. Develop high-performance data pipelines, enable multi-cloud interoperability (AWS/Azure), implement governance and secure access patterns, maintain CI/CD and infrastructure-as-code for platform provisioning, and lead technical design while mentoring engineers.

Define and maintain data architecture, integration, and load processes. Build, test, and optimize database technologies and SQL performance. Support security posture, troubleshoot implementations, and provide technical guidance. Drive process improvements and may lead small projects within the data domain.

Develop and maintain data extraction, transformation, automation, integration, and analytics solutions for Mastercard initiatives. Build complex SQL queries, Alteryx workflows, Tableau dashboards, and Power Platform applications. Gather requirements, deliver projects end to end, optimize reporting services, communicate quantitative insights, and collaborate with technical and business stakeholders to improve operational efficiency.

Designs and supports large-scale cloud-native data platforms on AWS and Databricks. Builds batch, streaming, and real-time pipelines; engineers distributed processing with Spark; develops Lakehouse architectures using Delta Lake and Iceberg; modernizes Hadoop workloads; and implements automation, governance, security, observability, performance optimization, and cost management. Partners with architects, product managers, data scientists, and engineers to enable analytics, machine learning, and AI at petabyte scale.